| |
| import os |
| import numpy as np |
| import warnings |
|
|
| try: |
| import mmcv |
| except ImportError: |
| warnings.warn( |
| "The module 'mmcv' is not installed. The package will have limited functionality. Please install it using the command: mim install 'mmcv>=2.0.1'" |
| ) |
|
|
| try: |
| from mmpose.apis import inference_topdown |
| from mmpose.apis import init_model as init_pose_estimator |
| from mmpose.evaluation.functional import nms |
| from mmpose.utils import adapt_mmdet_pipeline |
| from mmpose.structures import merge_data_samples |
| except ImportError: |
| warnings.warn( |
| "The module 'mmpose' is not installed. The package will have limited functionality. Please install it using the command: mim install 'mmpose>=1.1.0'" |
| ) |
| |
| try: |
| from mmdet.apis import inference_detector, init_detector |
| except ImportError: |
| warnings.warn( |
| "The module 'mmdet' is not installed. The package will have limited functionality. Please install it using the command: mim install 'mmdet>=3.1.0'" |
| ) |
|
|
|
|
| class Wholebody: |
| def __init__(self, |
| det_config=None, det_ckpt=None, |
| pose_config=None, pose_ckpt=None, |
| device="cpu"): |
| |
| if det_config is None: |
| det_config = os.path.join(os.path.dirname(__file__), "yolox_config/yolox_l_8xb8-300e_coco.py") |
| |
| if pose_config is None: |
| pose_config = os.path.join(os.path.dirname(__file__), "dwpose_config/dwpose-l_384x288.py") |
|
|
| if det_ckpt is None: |
| det_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth' |
| |
| if pose_ckpt is None: |
| pose_ckpt = "https://huggingface.co/wanghaofan/dw-ll_ucoco_384/resolve/main/dw-ll_ucoco_384.pth" |
| |
| |
| self.detector = init_detector(det_config, det_ckpt, device=device) |
| self.detector.cfg = adapt_mmdet_pipeline(self.detector.cfg) |
|
|
| |
| self.pose_estimator = init_pose_estimator( |
| pose_config, |
| pose_ckpt, |
| device=device) |
| |
| def to(self, device): |
| self.detector.to(device) |
| self.pose_estimator.to(device) |
| return self |
| |
| def __call__(self, oriImg): |
| |
| det_result = inference_detector(self.detector, oriImg) |
| pred_instance = det_result.pred_instances.cpu().numpy() |
| bboxes = np.concatenate( |
| (pred_instance.bboxes, pred_instance.scores[:, None]), axis=1) |
| bboxes = bboxes[np.logical_and(pred_instance.labels == 0, |
| pred_instance.scores > 0.5)] |
| |
| |
| bboxes = bboxes[nms(bboxes, 0.7), :4] |
|
|
| |
| if len(bboxes) == 0: |
| pose_results = inference_topdown(self.pose_estimator, oriImg) |
| else: |
| pose_results = inference_topdown(self.pose_estimator, oriImg, bboxes) |
| preds = merge_data_samples(pose_results) |
| preds = preds.pred_instances |
|
|
| |
| keypoints = preds.get('transformed_keypoints', |
| preds.keypoints) |
| if 'keypoint_scores' in preds: |
| scores = preds.keypoint_scores |
| else: |
| scores = np.ones(keypoints.shape[:-1]) |
|
|
| if 'keypoints_visible' in preds: |
| visible = preds.keypoints_visible |
| else: |
| visible = np.ones(keypoints.shape[:-1]) |
| keypoints_info = np.concatenate( |
| (keypoints, scores[..., None], visible[..., None]), |
| axis=-1) |
| |
| neck = np.mean(keypoints_info[:, [5, 6]], axis=1) |
| |
| neck[:, 2:4] = np.logical_and( |
| keypoints_info[:, 5, 2:4] > 0.3, |
| keypoints_info[:, 6, 2:4] > 0.3).astype(int) |
| new_keypoints_info = np.insert( |
| keypoints_info, 17, neck, axis=1) |
| mmpose_idx = [ |
| 17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3 |
| ] |
| openpose_idx = [ |
| 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17 |
| ] |
| new_keypoints_info[:, openpose_idx] = \ |
| new_keypoints_info[:, mmpose_idx] |
| keypoints_info = new_keypoints_info |
|
|
| keypoints, scores, visible = keypoints_info[ |
| ..., :2], keypoints_info[..., 2], keypoints_info[..., 3] |
| |
| return keypoints, scores |
|
|
